[Enhance] metainfo of dataset can be a generic dict-like Mapping

mmengine/dataset/base_dataset.py

@@ -4,11 +4,13 @@
 import gc
 import logging
 import pickle
+from collections.abc import Mapping
 from typing import Any, Callable, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
 from torch.utils.data import Dataset
 
+from mmengine.config import Config
 from mmengine.fileio import join_path, list_from_file, load
 from mmengine.logging import print_log
 from mmengine.registry import TRANSFORMS
@@ -155,8 +157,8 @@ class BaseDataset(Dataset):
 
     Args:
         ann_file (str, optional): Annotation file path. Defaults to ''.
-        metainfo (dict, optional): Meta information for dataset, such as class
-            information. Defaults to None.
+        metainfo (Mapping or Config, optional): Meta information for
+            dataset, such as class information. Defaults to None.
         data_root (str, optional): The root directory for ``data_prefix`` and
             ``ann_file``. Defaults to ''.
         data_prefix (dict): Prefix for training data. Defaults to
@@ -213,7 +215,7 @@ class BaseDataset(Dataset):
 
     def __init__(self,
                  ann_file: Optional[str] = '',
-                 metainfo: Optional[dict] = None,
+                 metainfo: Union[Mapping, Config, None] = None,
                  data_root: Optional[str] = '',
                  data_prefix: dict = dict(img_path=''),
                  filter_cfg: Optional[dict] = None,
@@ -472,13 +474,14 @@ def load_data_list(self) -> List[dict]:
         return data_list
 
     @classmethod
-    def _load_metainfo(cls, metainfo: dict = None) -> dict:
+    def _load_metainfo(cls,
+                       metainfo: Union[Mapping, Config, None] = None) -> dict:
         """Collect meta information from the dictionary of meta.
 
         Args:
-            metainfo (dict): Meta information dict. If ``metainfo``
-                contains existed filename, it will be parsed by
-                ``list_from_file``.
+            metainfo (Mapping or Config, optional): Meta information dict.
+                If ``metainfo`` contains existed filename, it will be
+                parsed by ``list_from_file``.
 
         Returns:
             dict: Parsed meta information.
@@ -487,9 +490,9 @@ def _load_metainfo(cls, metainfo: dict = None) -> dict:
         cls_metainfo = copy.deepcopy(cls.METAINFO)
         if metainfo is None:
             return cls_metainfo
-        if not isinstance(metainfo, dict):
-            raise TypeError(
-                f'metainfo should be a dict, but got {type(metainfo)}')
+        if not isinstance(metainfo, (Mapping, Config)):
+            raise TypeError('metainfo should be a Mapping or Config, '
+                            f'but got {type(metainfo)}')
 
         for k, v in metainfo.items():
             if isinstance(v, str):

tests/test_dataset/test_base_dataset.py

@@ -6,6 +6,7 @@
 import pytest
 import torch
 
+from mmengine.config import Config, ConfigDict
 from mmengine.dataset import (BaseDataset, ClassBalancedDataset, Compose,
                               ConcatDataset, RepeatDataset, force_full_init)
 from mmengine.registry import DATASETS, TRANSFORMS
@@ -202,6 +203,39 @@ def test_meta(self):
             task_name='new_task',
             classes=('dog', ),
             empty_list=[])
+
+        # test dataset.metainfo with passing metainfo as Config into
+        # self.base_dataset
+        metainfo = Config(dict(classes=('dog', ), task_name='new_task'))
+        dataset = BaseDataset(
+            data_root=osp.join(osp.dirname(__file__), '../data/'),
+            data_prefix=dict(img_path='imgs'),
+            ann_file='annotations/dummy_annotation.json',
+            metainfo=metainfo)
+        assert BaseDataset.METAINFO == dict(
+            dataset_type=dataset_type, classes=('dog', 'cat'))
+        assert dataset.metainfo == dict(
+            dataset_type=dataset_type,
+            task_name='new_task',
+            classes=('dog', ),
+            empty_list=[])
+
+        # test dataset.metainfo with passing metainfo as ConfigDict (Mapping)
+        # into self.base_dataset
+        metainfo = ConfigDict(dict(classes=('dog', ), task_name='new_task'))
+        dataset = BaseDataset(
+            data_root=osp.join(osp.dirname(__file__), '../data/'),
+            data_prefix=dict(img_path='imgs'),
+            ann_file='annotations/dummy_annotation.json',
+            metainfo=metainfo)
+        assert BaseDataset.METAINFO == dict(
+            dataset_type=dataset_type, classes=('dog', 'cat'))
+        assert dataset.metainfo == dict(
+            dataset_type=dataset_type,
+            task_name='new_task',
+            classes=('dog', ),
+            empty_list=[])
+
         # reset `base_dataset.METAINFO`, the `dataset.metainfo` should not
         # change
         BaseDataset.METAINFO['classes'] = ('dog', 'cat', 'fish')